System and method for determining pose data for a vehicle

ABSTRACT

Example systems and methods are disclosed for determining vehicle pose data for an autonomous vehicle. The vehicle computer system may receive pose data from multiple pose measurement systems of the autonomous vehicle. Each pose measurement system may include one or more corresponding sensors of the autonomous vehicle. The vehicle computer system may determine a pose data quality for the received pose data for each pose measurement system. The vehicle computer system may set the vehicle pose data to the pose data of the pose measurement system with the highest pose data quality. The vehicle computer system may control the autonomous vehicle based on the vehicle pose data.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.15/054,441, filed Feb. 26, 2016, which is incorporated herein byreference.

BACKGROUND

Unless otherwise indicated herein, the materials described in thissection are not prior art to the claims in this application and are notadmitted to be prior art by inclusion in this section.

A vehicle could be any wheeled, powered vehicle and may include a car,truck, motorcycle, bus, etc. Vehicles can be utilized for various taskssuch as transportation of people and goods, as well as many other uses.Some vehicles may be partially or fully autonomous. For instance, when avehicle is in an autonomous mode, some or all of the driving aspects ofvehicle operation can be handled by a vehicle control system. In suchcases, computing devices located onboard and/or in a server networkcould be operable to carry out functions such as planning a drivingroute, sensing aspects of the vehicle, sensing the environment of thevehicle, and controlling drive components such as steering, throttle,and brake. Thus, autonomous vehicles may reduce or eliminate the needfor human interaction in various aspects of vehicle operation.

SUMMARY

Example systems and methods may provide for determining vehicle posedata for an autonomous vehicle. The vehicle computer system of anautonomous vehicle may receive first pose data from a first posemeasurement system and second pose data from a second pose measurementsystem. Each pose measurement system may have one or more correspondingsensors of the autonomous vehicle. The vehicle computer system may thendetermine a pose data quality for each received pose data. Pose dataquality may be determined by crosschecking sensor data from the two posemeasurement systems, using Kalman filters, and/or using other methods tograde the pose data quality as “good,” “marginal,” or “bad.”

The vehicle pose data may be set to the first pose data quality when thefirst pose data is better than, or the same as, the second pose dataquality. Alternatively, the vehicle pose data may be changed from thefirst pose data to the second pose data when the second pose dataquality is better than the first pose data quality. Once the vehiclepose data is set, the vehicle computer system may control the autonomousvehicle based on the vehicle pose data. For example, the vehiclecomputer system may control the autonomous vehicle to stop within apredetermined period of time (such as 5 seconds, 15 seconds, 60 seconds,or some other amount of time) in response to the vehicle computer systemnot receiving vehicle pose data or receiving vehicle pose data with“bad” pose data quality.

In a first aspect, a method including receiving, at a vehicle computersystem for an autonomous vehicle, first pose data for the autonomousvehicle from a first pose measurement system of the autonomous vehicle,wherein the first pose measurement system includes one or morecorresponding sensors of the autonomous vehicle; receiving, at thevehicle computer system for the autonomous vehicle, second pose data forthe autonomous vehicle from a second pose measurement system of theautonomous vehicle, wherein the second pose measurement system includesone or more corresponding sensors of the autonomous vehicle; determininga first pose data quality for the received first pose data and a secondpose data quality for the received second pose data; setting the firstpose data as vehicle pose data for the autonomous vehicle in response tothe first pose data quality being better than or the same as the secondpose data quality; and controlling, by the vehicle computer system, theautonomous vehicle based on at least the vehicle pose data.

In a second aspect, a non-transitory computer-readable medium storinginstructions that are executable by one or more computing devices, whereexecuting the instructions causes the one or more computing devices toperform functions including receiving, at a vehicle computer system foran autonomous vehicle, first pose data for the autonomous vehicle from afirst pose measurement system of the autonomous vehicle, wherein thefirst pose measurement system includes one or more corresponding sensorsof the autonomous vehicle; receiving, at the vehicle computer system forthe autonomous vehicle, second pose data for the autonomous vehicle froma second pose measurement system of the autonomous vehicle, wherein thesecond pose measurement system includes one or more correspondingsensors of the autonomous vehicle; determining a first pose data qualityfor the received first pose data and a second pose data quality for thereceived second pose data; setting the first pose data as vehicle posedata for the autonomous vehicle in response to the first pose dataquality being better than or the same as the second pose data quality;and controlling, by the vehicle computer system, the autonomous vehiclebased on at least the vehicle pose data.

In a third aspect, a vehicle computer system for an autonomous vehicleincluding a processor and a memory storing instructions that whenexecuted by the processor causes the vehicle computing system to performfunctions including receiving first pose data for the autonomous vehiclefrom a first pose measurement system of the autonomous vehicle, whereinthe first pose measurement system includes one or more correspondingsensors of the autonomous vehicle; receiving second pose data for theautonomous vehicle from a second pose measurement system of theautonomous vehicle, wherein the second pose measurement system includesone or more corresponding sensors of the autonomous vehicle; determininga first pose data quality for the received first pose data and a secondpose data quality for the received second pose data; setting the firstpose data as vehicle pose data for the autonomous vehicle in response tothe first pose data quality being better than or the same as the secondpose data quality; and controlling the autonomous vehicle based on atleast the vehicle pose data.

In a fourth aspect, a system may include means for receiving, at avehicle computer system for an autonomous vehicle, first pose data forthe autonomous vehicle from a first pose measurement system of theautonomous vehicle, wherein the first pose measurement system includesone or more corresponding sensors of the autonomous vehicle. The systemmay also include means for receiving, at the vehicle computer system forthe autonomous vehicle, second pose data for the autonomous vehicle froma second pose measurement system of the autonomous vehicle, wherein thesecond pose measurement system includes one or more correspondingsensors of the autonomous vehicle. The system may additionally includemeans for determining a first pose data quality for the received firstpose data and a second pose data quality for the received second posedata. The system may also include means for setting the first pose dataas vehicle pose data for the autonomous vehicle in response to the firstpose data quality being better than or the same as the second pose dataquality. The system may further include means for controlling, by thevehicle computer system, the autonomous vehicle based on at least thevehicle pose data.

The foregoing summary is illustrative only and is not intended to be inany way limiting. In addition to the illustrative aspects, embodiments,and features described above, further aspects, embodiments, and featureswill become apparent by reference to the figures and the followingdetailed description and the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a functional block diagram illustrating a vehicle, accordingto an example embodiment.

FIG. 2 shows a vehicle, according to an example embodiment.

FIG. 3A is a vehicle computer system, according to an exampleembodiment.

FIG. 3B is a pose measurement system, according to an exampleembodiment.

FIG. 3C is a table indicating autonomous vehicle control based on posedata quality, according to an example embodiment.

FIG. 4 is a flowchart of an example method.

FIG. 5 illustrates an example computer readable medium.

DETAILED DESCRIPTION

Example methods and systems are described herein. Any example embodimentor feature described herein is not necessarily to be construed aspreferred or advantageous over other embodiments or features. Theexample embodiments described herein are not meant to be limiting. Itwill be readily understood that certain aspects of the disclosed systemsand methods can be arranged and combined in a wide variety of differentconfigurations, all of which are contemplated herein.

Furthermore, the particular arrangements shown in the Figures should notbe viewed as limiting. It should be understood that other embodimentsmight include more or less of each element shown in a given Figure.Further, some of the illustrated elements may be combined or omitted.Yet further, an example embodiment may include elements that are notillustrated in the Figures.

Autonomous vehicles (e.g., self-driving cars) may provide safer and moreefficient transportation than manually operated vehicles. In operation,autonomous vehicles may use data about their position, orientation, andvelocity (collectively known as pose) relative to the world. Autonomousvehicles may receive pose data from a pose measurement system thatincludes various sensors (e.g., an inertial measurement unit, a GPSreceiver, wheel speed sensors, etc.). Based on sensor data from thesesensors, the autonomous vehicle can determine its pose relative to theworld.

Efficient and safe transportation of an autonomous vehicle based on posedata may occasionally present challenges. There may be various potentialcauses of pose measurement system failure. There may be variouspotential causes of pose measurement system failure. These causes mayinclude sensor failure, hardware failure, software bugs, communicationfailure, loss of power, and or other types of failure. These failuresmay result in unreliable, “bad” quality pose data making operation ofthe autonomous vehicle challenging. Furthermore, even if the posemeasurement system fails and/or provides unreliable, “bad” quality posedata, it may still be desirable for the autonomous vehicle to continueto operate safely and/or efficiently. Accordingly, a system and methodare described that may address these and/or other challenges.

A method may be executed by a vehicle computer system of an autonomousvehicle. The vehicle computer system may receive first pose data from afirst pose measurement system and second pose data from a second posemeasurement system. Each of the first and second pose measurementsystems may have their own corresponding sensors. The vehicle computersystem may continue to execute the method by determining a first posedata quality for the first pose data and a second pose data quality forthe second pose data. The vehicle computer system may also set thevehicle pose data for the autonomous vehicle to the first pose databased on at least the first pose data quality and the second pose dataquality. For example, the vehicle computer system may determine that thefirst pose data quality is better than the second pose data quality, andthus, set the vehicle pose data for the autonomous vehicle to the firstpose data. Next, the vehicle computer system may control the autonomousvehicle based on the vehicle pose data. For example, the vehiclecomputer system may determine that the vehicle pose data has a pose dataquality of “good,” and thus, control the vehicle to continue drivingunder normal operation.

The first pose measurement system may determine pose data based on oneor more sensors corresponding to the first pose measurement system. Thesensors may include three-axis gyroscopes, three-axis accelerometers,IMUs, GPS receivers, wheel speed sensors, and/or other sensors of theautonomous vehicle. The pose measurement system may also employ aprocessor (CPU) to determine pose data. The pose data determined basedoff the sensors by the CPU may indicate the position, orientation, andvelocity of the autonomous vehicle relative to the world. The posemeasurement system may stop transmitting pose data to the vehiclecomputer system in response to failure of the corresponding sensorsand/or the CPU.

The vehicle computer system may receive pose data from two independentpose measurement systems to improve the quality of the pose datareceived and used by the vehicle computer system. In particular, if posedata from one pose measurement system is not sent or has a “bad” posedata quality, the vehicle computer system can instead rely on pose datafrom the other pose measurement system. Each pose measurement systemrelies on an independent communication channel to send pose data to thevehicle computer system. Each pose measurement system has one or morecorresponding sensors that are independent of the sensors of the otherpose measurement system. By relying on independent sensors, each posemeasurement system can provide an independent measurement of pose dataof the autonomous vehicle to the vehicle computer system.

Each pose measurement system may determine a pose data qualityassociated with the pose data before transmission to the vehiclecomputer system. The pose data quality, in one embodiment, may be ratedas “bad,” “marginal,” or “good.” The pose measurement system maydetermine pose data quality using various methods, includingcrosschecking of the corresponding sensors, verification that sensoroutputs are within plausible ranges, Kalman filter covariance tracking,and/or other measures of confidence in the received sensor data. Poorsensor crosschecks, implausible sensor outputs, and/or poor confidencein received data can cause the pose data quality to be downgraded to“bad” or “marginal.” In other embodiments, pose data quality may havemore, fewer, and/or different ratings than “bad,” “marginal,” and“good.”

The vehicle computer system may execute pose monitor software thatreceives pose data from the pose measurement systems and sets thevehicle pose data. The pose monitor software may receive a pose datamessage from each pose measurement system. A pose data message mayinclude pose data from the pose measurement system, pose data qualityfor the pose data, and a timestamp. The pose measurement system softwaremay receive a first pose data message from the first pose measurementsystem and a second pose data message from the second pose measurementsystem. The pose monitor software may use the data from both the firstand second pose data messages to update pose data quality from bothmessages.

The pose monitor software may alter the pose data quality of thereceived pose data in several instances. These instances can includeearly timestamps, late timestamps, and out-of-range pose data values(e.g., a pose data value of infinity). Another instance for downgradingpose data may occur if pose data from a pose measurement system isinconsistent. For example, if the pose data from a pose measurementsystem indicates a high velocity, but does not show the vehicle positionas changing over time, then the pose data from the pose measurementsystem may be considered inconsistent and result in the pose dataquality being adjusted to “bad.” Yet another instance for downgradingpose data may be in response to cross comparing first and second posedata. For example, if the first and second pose data are compared andthe data differ, then one or both of the corresponding pose data qualityvalues may be downgraded to “marginal” or “bad,” depending on theseverity of the disagreement. Other scenarios may exist in which thepose monitor software further adjusts the pose data quality of thereceived pose data.

Once the pose data quality has been adjusted, the pose monitor softwaremay set the vehicle pose data to the received pose data with the higherpose data quality. If the pose data quality of the first pose data andthe second pose data is the same, then the pose monitor software may notchange the source of the pose data. In other words, the vehicle posedata source may remain the first pose data until the second pose datahas a pose data quality that is better than the first vehicle pose data.

The pose monitor software may also determine and track the offsetbetween the first pose data and the second pose data. By tracking theoffset, the pose monitor software can smoothly transition the vehiclepose data from the first pose data to the second pose data. For example,if a first pose data quality is “bad” and includes a very differentposition (e.g., 10 miles) from the position of the second pose data, andthe second pose data quality is “good,” then the pose monitor softwaremay change the vehicle pose data from the first pose data to the secondpose data. However, a rapid, large change in the position of the vehiclepose data (e.g., 10 miles) may be detrimental to the autonomous vehicleoperating safely and efficiently. Thus, the pose monitor software mayuse the offset (e.g., 10 miles) to gradually adjust the position of thevehicle pose data from the first pose data position to the second posedata position over a period of time.

The vehicle computer system may rely on the vehicle pose data to controloperation of the autonomous vehicle. In some embodiments, the vehiclecomputer system may control autonomous vehicle operation based on thevehicle pose data quality. For example, if the latest vehicle pose datais too old, or has a vehicle pose data quality of “bad”, the vehiclecomputer system may control the autonomous vehicle to stop as quickly aspossible. If the latest vehicle pose data quality is “marginal”, thevehicle computer system may control the autonomous vehicle to park assoon as possible, park in the next 10 seconds, or to park afterfinishing the trip. If the vehicle pose data quality is “good”, then thevehicle computer system may control the autonomous vehicle to continuedriving under normal operation. Other embodiments are also possible. Byrelying on independent, redundant, pose measurement systems, theautonomous vehicle can operate efficiently and safely while relying onless expensive pose measurement system hardware.

Example systems within the scope of the present disclosure will now bedescribed in greater detail. An example system may be implemented in ormay take the form of an automobile. However, an example system may alsobe implemented in or take the form of other vehicles, such as cars,trucks, motorcycles, buses, boats, airplanes, helicopters, lawn mowers,earth movers, boats, snowmobiles, aircraft, recreational vehicles,amusement park vehicles, farm equipment, construction equipment, trams,golf carts, trains, and trolleys. Other vehicles are possible as well.

FIG. 1 is a functional block diagram illustrating a vehicle 100,according to an example embodiment. The vehicle 100 could be configuredto operate fully or partially in an autonomous mode. For example, acomputer system could control the vehicle 100 while in the autonomousmode, and may be operable to capture an image with a camera in vehicle100, analyze the image for the presence of a turn signal indicator, andresponsively control vehicle 100 based on the presence of the turnsignal indicator. While in autonomous mode, the vehicle 100 may beconfigured to operate without human interaction.

The vehicle 100 could include various subsystems such as a propulsionsystem 102, a sensor system 104, a control system 106, one or moreperipherals 108, as well as a power supply 110, a computer system 112, adata storage 114, and a user interface 116. The vehicle 100 may includemore or fewer subsystems and each subsystem could include multipleelements. Further, each of the subsystems and elements of vehicle 100could be interconnected. Thus, one or more of the described functions ofthe vehicle 100 may be divided up into additional functional or physicalcomponents, or combined into fewer functional or physical components. Insome further examples, additional functional and/or physical componentsmay be added to the examples illustrated by FIG. 1.

The propulsion system 102 may include components operable to providepowered motion for the vehicle 100. Depending upon the embodiment, thepropulsion system 102 could include an engine/motor 118, an energysource 119, a transmission 120, and wheels/tires 121. The engine/motor118 could be any combination of an internal combustion engine, anelectric motor, steam engine, Stirling engine, or some other engine.Other motors and/or engines are possible. In some embodiments, theengine/motor 118 may be configured to convert energy source 119 intomechanical energy. In some embodiments, the propulsion system 102 couldinclude multiple types of engines and/or motors. For instance, agas-electric hybrid car could include a gasoline engine and an electricmotor. Other examples are possible.

The energy source 119 could represent a source of energy that may, infull or in part, power the engine/motor 118. Examples of energy sources119 contemplated within the scope of the present disclosure includegasoline, diesel, other petroleum-based fuels, propane, other compressedgas-based fuels, ethanol, solar panels, batteries, and other sources ofelectrical power. The energy source(s) 119 could additionally oralternatively include any combination of fuel tanks, batteries,capacitors, and/or flywheels. The energy source 119 could also provideenergy for other systems of the vehicle 100.

The transmission 120 could include elements that are operable totransmit mechanical power from the engine/motor 118 to the wheels/tires121. The transmission 120 could include a gearbox, a clutch, adifferential, and a drive shaft. Other components of transmission 120are possible. The drive shafts could include one or more axles thatcould be coupled to the one or more wheels/tires 121.

The wheels/tires 121 of vehicle 100 could be configured in variousformats, including a unicycle, bicycle/motorcycle, tricycle, orcar/truck four-wheel format. Other wheel/tire geometries are possible,such as those including six or more wheels. Any combination of thewheels/tires 121 of vehicle 100 may be operable to rotate differentiallywith respect to other wheels/tires 121. The wheels/tires 121 couldrepresent at least one wheel that is fixedly attached to thetransmission 120 and at least one tire coupled to a rim of the wheelthat could make contact with the driving surface. The wheels/tires 121could include any combination of metal and rubber. Other materials arepossible.

The sensor system 104 may include several elements such as a GlobalPositioning System (GPS) 122, an inertial measurement unit (IMU) 124, aradar 126, a laser rangefinder/LIDAR 128, a camera 130, a steeringsensor 123, and a throttle/brake sensor 125. The sensor system 104 couldalso include other sensors, such as those that may monitor internalsystems of the vehicle 100 (e.g., O₂ monitor, fuel gauge, engine oiltemperature, brake wear).

The GPS 122 could include a transceiver operable to provide informationregarding the position of the vehicle 100 with respect to the Earth. TheIMU 124 could include a combination of accelerometers and gyroscopes andcould represent any number of systems that sense position andorientation changes of a body based on inertial acceleration.Additionally, the IMU 124 may be able to detect a pitch and yaw of thevehicle 100. The pitch and yaw may be detected while the vehicle isstationary or in motion.

The radar 126 may represent a system that utilizes radio signals tosense objects, and in some cases their speed and heading, within thelocal environment of the vehicle 100. Additionally, the radar 126 mayhave a plurality of antennas configured to transmit and receive radiosignals. The laser rangefinder/LIDAR 128 could include one or more lasersources, a laser scanner, and one or more detectors, among other systemcomponents. The laser rangefinder/LIDAR 128 could be configured tooperate in a coherent mode (e.g., using heterodyne detection) or in anincoherent detection mode. The camera 130 could include one or moredevices configured to capture a plurality of images of the environmentof the vehicle 100. The camera 130 could be a still camera or a videocamera.

The steering sensor 123 may represent a system that senses the steeringangle of the vehicle 100. In some embodiments, the steering sensor 123may measure the angle of the steering wheel itself. In otherembodiments, the steering sensor 123 may measure an electrical signalrepresentative of the angle of the steering wheel. Still, in furtherembodiments, the steering sensor 123 may measure an angle of the wheelsof the vehicle 100. For instance, an angle of the wheels with respect toa forward axis of the vehicle 100 could be sensed. Additionally, in yetfurther embodiments, the steering sensor 123 may measure a combination(or a subset) of the angle of the steering wheel, electrical signalrepresenting the angle of the steering wheel, and the angle of thewheels of vehicle 100.

The throttle/brake sensor 125 may represent a system that senses theposition of either the throttle position or brake position of thevehicle 100. In some embodiments, separate sensors may measure thethrottle position and brake position. In some embodiments, thethrottle/brake sensor 125 may measure the angle of both the gas pedal(throttle) and brake pedal. In other embodiments, the throttle/brakesensor 125 may measure an electrical signal that could represent, forinstance, an angle of a gas pedal (throttle) and/or an angle of a brakepedal. Still, in further embodiments, the throttle/brake sensor 125 maymeasure an angle of a throttle body of the vehicle 100. The throttlebody may include part of the physical mechanism that provides modulationof the energy source 119 to the engine/motor 118 (e.g., a butterflyvalve or carburetor). Additionally, the throttle/brake sensor 125 maymeasure a pressure of one or more brake pads on a rotor of vehicle 100.In yet further embodiments, the throttle/brake sensor 125 may measure acombination (or a subset) of the angle of the gas pedal (throttle) andbrake pedal, electrical signal representing the angle of the gas pedal(throttle) and brake pedal, the angle of the throttle body, and thepressure that at least one brake pad is applying to a rotor of vehicle100. In other embodiments, the throttle/brake sensor 125 could beconfigured to measure a pressure applied to a pedal of the vehicle, suchas a throttle or brake pedal.

The control system 106 could include various elements include steeringunit 132, throttle 134, brake unit 136, a sensor fusion algorithm 138, acomputer vision system 140, a navigation/pathing system 142, and anobstacle avoidance system 144. The steering unit 132 could represent anycombination of mechanisms that may be operable to adjust the heading ofvehicle 100. The throttle 134 could control, for instance, the operatingspeed of the engine/motor 118 and thus control the speed of the vehicle100. The brake unit 136 could be operable to decelerate the vehicle 100.The brake unit 136 could use friction to slow the wheels/tires 121. Inother embodiments, the brake unit 136 could convert the kinetic energyof the wheels/tires 121 to electric current.

A sensor fusion algorithm 138 could include, for instance, a Kalmanfilter, Bayesian network, or other algorithm that may accept data fromsensor system 104 as input. The sensor fusion algorithm 138 couldprovide various assessments based on the sensor data. Depending upon theembodiment, the assessments could include evaluations of individualobjects and/or features, evaluation of a particular situation, and/orevaluate possible impacts based on the particular situation. Otherassessments are possible.

The computer vision system 140 could include hardware and softwareoperable to process and analyze images in an effort to determineobjects, important environmental features (e.g., stop lights, road wayboundaries, etc.), and obstacles. The computer vision system 140 coulduse object recognition, Structure From Motion (SFM), video tracking, andother algorithms used in computer vision, for instance, to recognizeobjects, map an environment, track objects, estimate the speed ofobjects, etc.

The navigation/pathing system 142 could be configured to determine adriving path for the vehicle 100. The navigation/pathing system 142 mayadditionally update the driving path dynamically while the vehicle 100is in operation. In some embodiments, the navigation/pathing system 142could incorporate data from the sensor fusion algorithm 138, the GPS122, and known maps so as to determine the driving path for vehicle 100.

The obstacle avoidance system 144 could represent a control systemconfigured to evaluate potential obstacles based on sensor data andcontrol the vehicle 100 to avoid or otherwise negotiate the potentialobstacles.

Various peripherals 108 could be included in vehicle 100. For example,peripherals 108 could include a wireless communication system 146, atouchscreen 148, a microphone 150, and/or a speaker 152. The peripherals108 could provide, for instance, means for a user of the vehicle 100 tointeract with the user interface 116. For example, the touchscreen 148could provide information to a user of vehicle 100. The user interface116 could also be operable to accept input from the user via thetouchscreen 148. In other instances, the peripherals 108 may providemeans for the vehicle 100 to communicate with devices within itsenvironment.

In one example, the wireless communication system 146 could beconfigured to wirelessly communicate with one or more devices directlyor via a communication network. For example, wireless communicationsystem 146 could use 3G cellular communication, such as CDMA, EVDO,GSM/GPRS, or 4G cellular communication, such as WiMAX or LTE.Alternatively, wireless communication system 146 could communicate witha wireless local area network (WLAN), for example, using WiFi. In someembodiments, wireless communication system 146 could communicatedirectly with a device, for example, using an infrared link, Bluetooth,or ZigBee. Other wireless protocols, such as various vehicularcommunication systems, are possible within the context of thedisclosure. For example, the wireless communication system 146 couldinclude one or more dedicated short range communications (DSRC) devicesthat could include public and/or private data communications betweenvehicles and/or roadside stations.

The power supply 110 may provide power to various components of vehicle100 and could represent, for example, a rechargeable lithium-ion orlead-acid battery. In an example embodiment, one or more banks of suchbatteries could be configured to provide electrical power. Other powersupply materials and types are possible. Depending upon the embodiment,the power supply 110, and energy source 119 could be integrated into asingle energy source, such as in some all-electric cars.

Many or all of the functions of vehicle 100 could be controlled bycomputer system 112. Computer system 112 may include at least oneprocessor 113 (which could include at least one microprocessor) thatexecutes instructions 115 stored in a non-transitory computer readablemedium, such as the data storage 114. The computer system 112 may alsorepresent a plurality of computing devices that may serve to controlindividual components or subsystems of the vehicle 100 in a distributedfashion.

In some embodiments, data storage 114 may contain instructions 115(e.g., program logic) executable by the processor 113 to execute variousfunctions of vehicle 100, including those described above in connectionwith FIG. 1. Data storage 114 may contain additional instructions aswell, including instructions to transmit data to, receive data from,interact with, and/or control one or more of the propulsion system 102,the sensor system 104, the control system 106, and the peripherals 108.

In addition to the instructions 115, the data storage 114 may store datasuch as roadway map data 166, path information, among other information.Such information may be used by vehicle 100 and computer system 112during the operation of the vehicle 100 in the autonomous,semi-autonomous, and/or manual modes.

The vehicle 100 may include a user interface 116 for providinginformation to or receiving input from a user of vehicle 100. The userinterface 116 could control or enable control of content and/or thelayout of interactive images that could be displayed on the touchscreen148. Further, the user interface 116 could include one or moreinput/output devices within the set of peripherals 108, such as thewireless communication system 146, the touchscreen 148, the microphone150, and the speaker 152.

The computer system 112 may control the function of the vehicle 100based on inputs received from various subsystems (e.g., propulsionsystem 102, sensor system 104, and control system 106), as well as fromthe user interface 116. For example, the computer system 112 may utilizeinput from the sensor system 104 in order to estimate the outputproduced by the propulsion system 102 and the control system 106.Depending upon the embodiment, the computer system 112 could be operableto monitor many aspects of the vehicle 100 and its subsystems. In someembodiments, the computer system 112 may disable some or all functionsof the vehicle 100 based on signals received from sensor system 104.

The components of vehicle 100 could be configured to work in aninterconnected fashion with other components within or outside theirrespective systems. For instance, in an example embodiment, the camera130 could capture a plurality of images that could represent informationabout a state of an environment of the vehicle 100 operating in anautonomous mode. The state of the environment could include parametersof the road on which the vehicle is operating. For example, the computervision system 140 may be able to recognize the slope (grade) or otherfeatures based on the plurality of images of a roadway. Additionally,the combination of Global Positioning System 122 and the featuresrecognized by the computer vision system 140 may be used with map data166 stored in the data storage 114 to determine specific roadparameters. Further, the radar unit 126 may also provide informationabout the surroundings of the vehicle.

In other words, a combination of various sensors (which could be termedinput-indication and output-indication sensors) and the computer system112 could interact to provide an indication of an input provided tocontrol a vehicle or an indication of the surroundings of a vehicle.

The computer system 112 could carry out several determinations based onthe indications received from the input- and output-indication sensors.For example, the computer system 112 could calculate the direction (i.e.angle) and distance (i.e. range) to one or more objects that arereflecting radar signals back to the radar unit 126. Additionally, thecomputer system 112 could calculate a range of interest. The range ofinterest could, for example, correspond to a region where the computersystem 112 has identified one or more targets of interest. Additionallyor additionally, the computer system 112 may identify one or moreundesirable targets. Thus, a range of interest may be calculated so asnot to include undesirable targets.

In some embodiments, the computer system 112 may make a determinationabout various objects based on data that is provided by systems otherthan the radar system. For example, the vehicle may have lasers or otheroptical sensors configured to sense objects in a field of view of thevehicle. The computer system 112 may use the outputs from the varioussensors to determine information about objects in a field of view of thevehicle. The computer system 112 may determine distance and directioninformation to the various objects. The computer system 112 may alsodetermine whether objects are desirable or undesirable based on theoutputs from the various sensors.

Although FIG. 1 shows various components of vehicle 100, i.e., wirelesscommunication system 146, computer system 112, data storage 114, anduser interface 116, as being integrated into the vehicle 100, one ormore of these components could be mounted or associated separately fromthe vehicle 100. For example, data storage 114 could, in part or infull, exist separate from the vehicle 100. Thus, the vehicle 100 couldbe provided in the form of device elements that may be locatedseparately or together. The device elements that make up vehicle 100could be communicatively coupled together in a wired and/or wirelessfashion.

FIG. 2 shows a vehicle 200 that could be similar or identical to vehicle100 described in reference to FIG. 1. Depending on the embodiment,vehicle 200 could include a sensor unit 202, a wireless communicationsystem 208, a radar 206, a laser rangefinder 204, and a camera 210. Theelements of vehicle 200 could include some or all of the elementsdescribed for FIG. 1. Although vehicle 200 is illustrated in FIG. 2 as acar, other embodiments are possible. For instance, the vehicle 200 couldrepresent a truck, a van, a semi-trailer truck, a motorcycle, a golfcart, an off-road vehicle, or a farm vehicle, among other examples.

The sensor unit 202 could include one or more different sensorsconfigured to capture information about an environment of the vehicle200. For example, sensor unit 202 could include any combination ofcameras, radars, LIDARs, range finders, and acoustic sensors. Othertypes of sensors are possible. Depending on the embodiment, the sensorunit 202 could include one or more movable mounts that could be operableto adjust the orientation of one or more sensors in the sensor unit 202.In one embodiment, the movable mount could include a rotating platformthat could scan sensors so as to obtain information from each directionaround the vehicle 200. In another embodiment, the movable mount of thesensor unit 202 could be moveable in a scanning fashion within aparticular range of angles and/or azimuths. The sensor unit 202 could bemounted atop the roof of a car, for instance, however other mountinglocations are possible. Additionally, the sensors of sensor unit 202could be distributed in different locations and need not be collocatedin a single location. Some possible sensor types and mounting locationsinclude radar 206 and laser rangefinder 204.

The wireless communication system 208 could be located as depicted inFIG. 2. Alternatively, the wireless communication system 208 could belocated, fully or in part, elsewhere. The wireless communication system208 may include wireless transmitters and receivers that could beconfigured to communicate with devices external or internal to thevehicle 200. Specifically, the wireless communication system 208 couldinclude transceivers configured to communicate with other vehiclesand/or computing devices, for instance, in a vehicular communicationsystem or a roadway station. Examples of such vehicular communicationsystems include dedicated short range communications (DSRC), radiofrequency identification (RFID), and other proposed communicationstandards directed towards intelligent transport systems.

The camera 210 could be mounted inside a front windshield of the vehicle200. The camera 210 could be configured to capture a plurality of imagesof the environment of the vehicle 200. Specifically, as illustrated, thecamera 210 could capture images from a forward-looking view with respectto the vehicle 200. Other mounting locations and viewing angles ofcamera 210 are possible. The camera 210 could represent one or morevisible light cameras. Alternatively or additionally, camera 210 couldinclude infrared sensing capabilities. The camera 210 could haveassociated optics that could be operable to provide an adjustable fieldof view. Further, the camera 210 could be mounted to vehicle 200 with amovable mount that could be operable to vary a pointing angle of thecamera 210.

FIG. 3A shows a vehicle computer system communication environment 300 a.The environment 300 a includes a vehicle computer system 305 a, a firstpose measurement system 311 a, a second pose measurement system 312 a, afirst communication channel 321 a, and a second communication channel322 a. In some embodiments, the environment 300 a may include more,fewer, and/or different components than those displayed in FIG. 3A.

FIG. 3B shows an example pose measurement system 311 b. The posemeasurement system 311 b may include a pose measurement system CPU 331b, an IMU 341 b, a wheel speed sensor 342 b, and a GPS receiver 343 b.In other embodiments, the pose measurement system 311 b may includemore, fewer, and/or different components than those displayed in FIG.3B.

Pose measurement system 311 b may correspond to pose measurement systemsin FIG. 3A (e.g., pose measurement systems 311 a and 312 a). Posemeasurement system 311 b determines pose data (e.g., position,orientation, and velocity relative to the world) of an autonomousvehicle. Pose measurement system 311 b also determines pose data qualityfor the determined pose data. Pose measurement system 311 b may transmitthe pose data and pose data quality to a vehicle computer system (suchas the vehicle computer system 305 a of FIG. 3A) to improve control ofthe autonomous vehicle. The pose measurement system 311 b may rely onpose measurement system CPU 331 b to execute some or all of the abovefunctions.

The pose measurement system CPU 331 b may determine pose data for anautonomous vehicle based on one or more corresponding sensors of thepose measurement system 311 b. Additionally, CPU 331 b may determine thepose data quality of the determined pose data, and transmit the posedata and pose data quality to the vehicle computer system (e.g., vehiclecomputer system 305 a in FIG. 3A) for further processing. The posemeasurement system CPU 331 b may be a dual-core, lockstep CPU that issafety rated for automotive vehicles. However, other types of CPUs maybe used for the pose measurement CPU 331 b.

The CPU 331 b may stop transmission of pose data to the vehicle computersystem in a few instances. In one instance, if a temporary or permanenthardware failure occurs (e.g., a cosmic ray) that causes one of thecores to act differently from the other core, the CPU may stoptransmitting pose data to the vehicle computer system. In anotherinstance, the CPU may perform multiple software checks to verify thatthe CPU may continue transmitting pose data. If one of the softwarechecks fails, then the CPU may stop transmitting pose data to thevehicle computer system. For example, if pose data is generated thatincludes floating point NaN values (Not a Number values), out of rangedata values, values of infinity, or other unexpected values, the CPU 331b may stop transmitting pose data to the vehicle computer system. Bystopping transmission of pose data in these instances, the CPU 331 bprevents the vehicle computer system from receiving unexpected valuesfor pose data that may diminish control of the autonomous vehicle.

Pose measurement system 311 b may also include one or more correspondingsensors. In FIG. 3B, the pose measurement system 311 b include sensorsIMU 341 b, wheel speed sensor 342 b, and GPS receiver 343 b. The sensorsof the system 311 b may provide data to CPU 331 b for determining posedata for the autonomous vehicle. In some embodiments, the posemeasurement system 311 b includes more, fewer, and/or different sensorsthan those displayed. Additionally, the system 311 b may includeduplicate sensors. For example, the system 311 b may include multipleIMUs 341 b. In this case, the pose measurement system 311 b may stillcontinue providing pose data when an IMU sensor fails because the posemeasurement system CPU 331 b receives data from the other IMU sensors.

The IMU 341 b may include gyroscopes, accelerometers, and other devicesto provide data about the position, velocity, and acceleration of theautonomous vehicle. IMU 341 b may correspond to inertial measurementunit 124 of FIG. 1, in some embodiments. Although FIG. 3B only displaysone IMU sensor 341 b, in some embodiments, a pose measurement system 311b may include multiple IMUs 341 b. CPU 331 b may use the IMU data todetermine pose data for the autonomous vehicle. In other embodiments,other sensors may provide position, velocity, and/or acceleration dataof the autonomous vehicle to the vehicle computer system.

Wheel speed sensors 342 b may also provide data to the pose measurementCPU 331 b to determine pose data for the pose measurement system 311 bof the autonomous vehicle. The wheel speed sensors 342 b may indicatethe rotational speed of each tire of the autonomous vehicle to the CPU331 b. The data provided by the wheel speed sensors 342 b may improvethe pose data determined by the CPU 331 b. For example, the wheel speedsensor data may be compared and/or crosschecked with the IMU data by theCPU 331 b.

In some embodiments, the wheel speed sensors 342 b may be shared bymultiple pose measurement systems, such as pose measurement systems 311a and 312 a. In other words, common wheel speed sensors 342 b mayprovide data to multiple pose measurement systems, such as systems 311 aand 312 a. Using common wheel speed sensors with multiple posemeasurement systems may reduce monetary costs for the sensors used todetermine pose data for the autonomous vehicle. However, in otherembodiments, each pose measurement system may have its own set ofcorresponding wheel speed sensors. In other embodiments, differentsensors may be used in combination with IMU data to improve the posedata determined by the CPU 331 b for system 311 b.

A GPS receiver may also provide data to the CPU 331 b for determiningpose data of the autonomous vehicle by pose measurement system 311 b.The GPS receiver 343 b may correspond to the GPS 122 of FIG. 1. The GPSreceiver data 343 b may further allow the CPU 331 b to crosscheck datareceived from IMU 341 b and wheel speed sensors 342 b to betterdetermine pose data for the autonomous vehicle by pose measurementsystem 311 b. In other embodiments, other sensors may be used fordetermining pose data.

The pose measurement system CPU 331 b may also determine the pose dataquality of the determined pose data. Pose data quality may indicate thereliability of the pose data. In some embodiments, the pose data qualitymay be determined to be either “good,” “marginal,” or “bad.” In otherembodiments, pose data quality determination may be more granular. Forexample, a pose data quality percentage value may indicate pose dataquality, with 0% indicating the lowest pose data quality and 100%indicating the highest pose data quality. Other metrics, scales, scoringsystems, rating systems, judgments, and/or quality measurement systemsmay be used for determining pose data quality. The vehicle computersystem may determine whether to use the pose data for autonomous vehiclecontrol based on the pose data quality. In other embodiments, pose dataquality may be used for other purposes.

Pose data quality may be degraded from “good” to “marginal” or “bad” fora variety of reasons. Sensor failure (e.g., accelerometers, gyroscopes,your sensor, GPS receiver, wheel speed sensor, etc.) that occursgradually or quickly may lead to degraded pose data quality. Otherreasons that pose data quality may be downgraded include loss of datadue to poor data communication connections, hardware failures,mechanical failures, skidding tires, and/or other reasons. In response,the CPU 331 b may rely on a variety of methods to determine pose dataquality.

First, CPU 331 b may rely on Kalman filters to determine pose dataquality. Various types of data (e.g., sensor data from IMUs 341 b, wheelspeed sensors 342 b, and/or GPS receivers 343 b) may be inputted into aKalman filter. The Kalman filter may be used to determine the covarianceof the data, which may indicate an uncertainty of the data. Inparticular, a higher covariance may indicate a higher uncertainty of thedata. Thus, sensor data inputted into a Kalman filter that results in ahigh covariance may cause the pose data quality to be downgraded from“good” quality to “marginal” or “bad” pose data quality. Other methodsfor determining uncertainty and/or confidence data may be used todetermine pose data quality.

Second, CPU 331 b may check to determine whether the received sensordata is out of range. For example, IMU 341 b may provide sensor dataindicating velocity of the autonomous vehicle. The sensor dataindicating velocity may have a valid range from 0 miles per hour to 85miles per hour in a particular direction. Thus, if sensor data isreceived indicating a velocity of 100 miles per hour, the pose dataquality may be downgraded to “marginal” or “bad” by the CPU 331 b. Otherexamples are also possible.

Third, CPU 331 b may crosscheck received data to determine the qualityof the pose data. For example, CPU 331 b may crosscheck velocity valuesof the autonomous vehicle determined based on different sensor data,such as IMU 341 b, wheel speed sensor 342 b, and GPS receiver 343 b. Ifthe sensor data from the wheel speed sensor 342 b and the GPS receiver343 b indicate that the vehicle is stationary (e.g., a vehicle velocityof 0 miles per hour in all directions) and the IMU 341 b indicates avehicle velocity of 10 miles per hour East, then the CPU 331 b maydetermine that the crosscheck has failed. In response to the failedcrosscheck, the CPU 331 b may downgrade the pose data quality from“good” quality to “marginal” or “bad” pose data quality. The CPU 331 bmay crosscheck received data with values from other sensors (notdisplayed) or derived values. For example, a derived value (such asexpected turn radius of the vehicle) may be crosschecked with sensordata (such as turn rate from IMU 341 b) to further determine the qualityof the pose data. Other examples are possible.

Referring to FIG. 3A, the vehicle computer system 305 a determines whichpose data from pose measurement systems to set as the vehicle pose dataand controls operation of the autonomous vehicle based on the setvehicle pose data. In some embodiments, the vehicle computer system 305a may correspond to the computer system 112 of FIG. 1. The vehiclecomputer system 305 a may include pose monitor software that receivespose data for the autonomous vehicle from multiple pose measurementsystems. The pose monitor software may determine which pose data to setas the vehicle pose data and monitor the pose data offset between thereceived pose data. By receiving pose data from multiple posemeasurement systems (e.g., systems 311 a and 312 a from FIG. 3A), thevehicle computer system 305 a can better determine whether reliable posedata is available for control of the autonomous vehicle. Accordingly,this improves control of the autonomous vehicle, as described in FIG.3A.

The vehicle computer system 305 a receives pose data from twoindependent pose measurement systems: pose measurement system 311 a andpose measurement system 312 a. In some embodiments, using twoindependent pose measurement systems (as opposed to three or moresystems) may be a cost-effective solution for determining vehicle posedata with independent, redundant pose measurement systems. In someembodiments, the vehicle computer system 305 a may receive pose datafrom more than two independent pose measurement systems.

Redundant, independent pose measurement systems allow the vehiclecomputer system 305 a to determine vehicle pose data for the autonomousvehicle despite hardware and/or software failures resulting in“marginal” and/or “bad” quality pose data. For example, if one posemeasurement system fails or provides “bad” quality pose data (e.g.,system 311 a), the other pose measurement system (e.g., system 312 a)may still provide “good” quality pose data for the vehicle. Thus, thevehicle computer system may still control the autonomous vehicle todrive under normal operations using vehicle pose data with a “good” posedata quality. If the vehicle computer system 305 a could only rely onthe failed pose measurement system or “bad” quality pose data, thevehicle computer system 305 a may be forced to control the autonomousvehicle to cease normal driving operation and stop and park. Thus,independent, redundant pose measurement systems may improve control andoperation of the autonomous vehicle by the vehicle computer system 305a.

Independent pose measurement systems may be independent for a fewreasons. First, as shown in FIG. 3B, each pose measurement system (e.g.,systems 311 a and 312 a from FIG. 3A) may include a pose measurementsystem CPU 331 b. If the CPU 331 b fails for one pose measurement system(e.g., system 311 a from FIG. 3A), the CPU 331 b for the other posemeasurement system (e.g. system 311 b from FIG. 3B) remains unaffectedand may still determine and transmit pose data with a “good” pose dataquality.

Second, each pose measurement system may include one or morecorresponding sensors. For example, in FIG. 3B, each pose measurementsystem (such as systems 311 a and 311 b) may include one or morecorresponding sensors, such as IMU 341 b, wheel speed sensors 342 b, andGPS receiver 343 b. If the IMU 341 b for system 311 a from FIG. 3Afails, the pose measurement system 312 a from FIG. 3A may still rely onits own corresponding IMU sensor 341 b to determine pose data with a“good” pose data quality. Although some sensors may be shared by thepose measurement systems 311 a and 312 a in some embodiments (e.g.,wheel speed sensors 342 b and/or GPS receiver 343 b from FIG. 3B), inother embodiments, each pose measurement system 311 a and 312 a mayinclude their own corresponding sensors. Other embodiments are possible.

Third, each independent pose measurement system 311 a and 312 a may relyon its own independent communication channel to transmit a pose datamessage from the CPU 331 b of the pose measurement system to the vehiclecomputer system 305 a. In particular, pose measurement system 311 asends pose data messages to vehicle computer system 305 a viaindependent communication channel 321 a. Pose measurement system 312 asends pose data messages to vehicle computer system 305 a viaindependent communication channel 322 a. As a result, if communicationchannel 321 a fails, pose measurement system 312 a is unaffected and canstill transmit pose data messages to vehicle computer system 305 a viacommunication channel 322 a. As a result, vehicle computer system 305 acan still set the vehicle pose data to pose data with a “good” pose dataquality that is received from system 312 a, despite the failure of thecommunication channel 321 a. By using independent communicationchannels, independent pose measurement system CPUs, and correspondingsensors for each pose measurement system, independent and redundant posemeasurement systems can enable the vehicle computer system to continuenormal driving operation for an autonomous vehicle and withstandhardware failures and/or “bad” quality pose data.

Each pose measurement system 311 a and 312 a transmits pose datamessages to the vehicle computer system 305 via its independentcommunication channel (e.g., communication channels 321 a and 322 a,respectively). Each pose data message includes pose data determined bythe corresponding pose measurement system, pose data quality of thetransmitted pose data, and a timestamp indicating a time approximatingwhen the message was generated by the pose measurement system. Thetimestamp may be used by the vehicle computer system 305 a to downgradepose data quality for received pose data due to the pose data being tooold. In other embodiments, the pose data message may include more,fewer, and/or different types of information.

FIG. 3C displays a table 351 c indicating autonomous vehicle controlbased on vehicle pose data of the autonomous vehicle. The table 351 cincludes a first pose measurement system pose data quality column 352 c,a second pose measurement system pose data quality column 353 c, avehicle pose data column 354 c, and a control output column 355 c. Inother embodiments, the table 351 c may include more, fewer, and/ordifferent columns, rows, and/or table entries than those displayed inFIG. 3A.

Table 351 c includes pose measurement system pose data quality column352 c. Column 352 c indicates the pose data quality of pose datareceived by a pose measurement system, such as pose measurement system311 a from FIG. 3A. In table 351 c, the pose data received from posemeasurement system number one has a pose data quality of “good” in rowsone, two, and three, a pose data quality of “marginal” in rows four,five, and six, and a pose data quality of “bad” in rows seven, eight,and nine. Other entries in table 351 c are possible.

Table 351 c also includes pose measurement system pose data qualitycolumn 353 c. Column 353 c indicates the pose data quality of pose datareceived by pose measurement system, such as pose measurement system 312a from FIG. 3A. The pose measurement system for column 353 c isdifferent from the pose measurement system for column 352 c. In table351 c, pose data received from pose measurement system number two has apose data quality of “good” in rows one, four, and seven, a pose dataquality of “marginal” in rows two, five, and eight, and pose dataquality of “bad” in rows three, six, and nine. Other entries in table351 c are also possible.

After pose data quality is determined by CPU 331 b of a respective posemeasurement system (e.g., systems 311 a and 312 a from FIG. 3A), thevehicle computer system 305 a may process the received pose data fromboth pose measurement systems and update the pose data quality of thereceived pose data. The vehicle computer system may rely on severalmethods to update pose data quality of received pose data. Similar toCPU 331 b, the vehicle computer system 305 a may first verify thereceived pose data is a plausible data value within the range of thesensor data. For example, values of infinity, not a number (Not aNumber), or out of range values, similar to the example discussed forCPU 331 b, would cause the vehicle computer system to update the valuewith a plausible value within the data range of the sensor. The vehiclecomputer system may also change the pose data quality to “bad” for thecorresponding pose data.

The vehicle computer system may also update pose data quality for posedata based on the timestamp of the received pose data message. Inparticular, pose data quality may be downgraded by the vehicle computersystem to “bad” when the pose data message reception time is too far inthe past, when the timestamp indicates a time too far in the past, orwhen the timestamp indicates a time in the future. Other embodiments arealso possible.

The vehicle computer system may also verify that the pose data receivedby a particular pose measurement system over a period of time isconsistent. For example, the vehicle computer system 305 a may verifythat pose data received over a period of time from pose measurementsystem 311 a is consistent. In one example, vehicle computer system 305a may receive pose data from system 311 a over a period of timeindicating a high velocity for the autonomous vehicle during that timeperiod. However, the pose data may also indicate that the autonomousvehicle position hasn't changed during the time period. As a result, thevehicle computer system 305 a may determine that the pose data receivedfrom system 311 a during the time period is inconsistent. The vehiclecomputer system may also update the pose data quality to “bad” for thereceived pose data from system 311 a.

The vehicle computer system may also crosscheck sensor data receivedfrom both pose measurement systems. In some embodiments, the vehiclecomputer system only crosschecks data between the pose measurementsystems when neither of the pose measurement systems generate pose datawith a “bad” pose data quality. When the sensor data of the twodifferent pose measurement systems are crosschecked, the pose dataquality of the compared pose data may be downgraded to “marginal” or“bad”, depending on the magnitude of the difference in the pose data.

For example, if the pose measurement system 311 a indicates that theautonomous vehicle is traveling 30 miles per hour, while the posemeasurement system 312 a indicates that the autonomous vehicle isstationary, the pose data quality of the pose data received from 311 aand/or 312 a may be downgraded to “bad”. Alternatively, if system 311 aindicates a vehicle velocity of 30 miles per hour, and system 312 aindicates a slightly different vehicle velocity (e.g., 27 miles perhour), the pose data quality of the pose data received from 311 a, 312a, or both 311 a and 312 a may be downgraded to “marginal.” The vehiclecomputer system may also crosscheck received data with values from othersensors that are not part of a pose measurement system (not displayed),derived values, or other values. For example, a derived value (such asexpected turn radius of the vehicle) may be crosschecked with sensordata (such as turn rate from an IMU of a pose measurement system) tofurther determine the quality of the pose data. Other embodiments arealso possible.

In some embodiments, the vehicle computer system may receive and/orstore additional information about pose data with a “marginal” pose dataquality. The additional information may indicate why the pose dataquality was downgraded to “marginal.” The additional information may bereceived as part of the pose data message, or in a separate message. Theadditional information may be received from a pose measurement system.By receiving and/or storing the additional information, the vehiclecomputer system may generate different control outputs for the same posedata quality (e.g., “marginal” pose data quality).

For example, additional information may be received and/or stored by thevehicle computer system from a pose measurement system. The additionalinformation may indicate that the pose data quality for pose data wasdowngraded to “marginal” due to a gradually failing yaw sensor. Inparticular, the quality may be downgraded to “marginal” because the yawsensor failed by an amount greater than a first threshold. For anotherexample, the additional information stored may indicate the pose dataquality for pose data was downgraded to “marginal” because the yawsensor failed by an amount larger than a second threshold that exceedsthe first threshold. In other embodiments, additional information may bestored for other pose data quality values, such as “good” and “bad” posedata quality, to enable the vehicle computer system to generate multiplecontrol outputs for pose data with “good” or “bad” pose data quality.The description accompanying FIG. 3C explains how multiple controloutputs can be generated for a pose data quality value based on thereceived and/or stored additional information.

Column 354 c indicates the pose data set as the vehicle pose data by thevehicle computer system (e.g., vehicle computer system 305 a from FIG.3A). The pose data can be pose data received by pose measurement systemnumber one from column 352 c or pose data received from pose measurementsystem number two from column 353 c. A table entry in column 354 c of“pose data #1” indicates that the vehicle pose data is set to the posedata received from pose measurement system number one of column 352 c. Atable entry of “pose data #2” in column 354 c indicates that the vehiclepose data is set to the pose data received from pose measurement systemnumber two for column 353 c. A table entry of “pose data number one orpose data #2” in column 354 c indicates that the vehicle pose data canbe set to the pose data received from either pose measurement systemnumber one for column 352 c or pose measurement system number two forcolumn 353 c. In table 351 c, the vehicle pose data of column 354 c isset to “pose data number #1” for rows two, three, and six. In table 351c, the vehicle pose data of column 354 c is set to “pose data number #2”for rows four, seven, and eight. In table 351 c, the vehicle pose dataof column 354 c is set to “pose data #1 or pose data #2” for rows one,five, and nine. Other table entries and other embodiments are alsopossible.

The vehicle computer system 305 a may set the vehicle pose data to posedata received from either the first pose measurement system or thesecond pose measurement system based on the pose data quality of thereceived pose data. The vehicle computer system 305 a may set vehiclepose data at a frequency of 1000 Hz. For example, the vehicle computersystem may set the vehicle pose data to the received pose data with thehighest pose data quality. In FIG. 3C, “good” pose data quality is thehighest pose data quality, “bad” pose data quality is the lowest posedata quality, and “marginal” pose data quality is in between “good” and“bad” pose data quality. If the pose data received from both posemeasurement systems have the same pose data quality, then the vehiclecomputer system may select pose data from either pose measurementsystem. In one embodiment, if the pose data received from both posemeasurements systems have the same pose data quality, then the vehiclecomputer system selects pose data from the pose measurement system thatprovided the last set of pose data. In other words, the vehicle computersystem 305 a may switch the source of the vehicle pose data when posedata quality of pose data received from the current source posemeasurement system is less than the pose data quality of pose datareceived from a different pose measurement system.

For example, if the vehicle computer system 305 a initially sets thevehicle pose data to pose data received from pose measurement systemnumber one, the vehicle computer system 305 a will continue to set thevehicle pose data as “pose data #1” until pose data received from thepose measurement system number two has a higher pose data quality (seecolumn 353 c) than the pose data quality of the pose data received fromthe first pose measurement system (see column 352 c). Thus, referring toFIG. 3C, in table 351 c, the vehicle computer system changes the vehiclepose data from pose data number one to pose data number two for rowsfour, seven, and eight because the pose data quality of the pose datafrom pose measurement system number two (see column 353 c) is higherthan the pose data quality of the pose data received from posemeasurement system number one (see column 352 c). Other embodiments arealso possible.

The vehicle computer system 305 a may determine and monitor a pose dataoffset to assist with changing the vehicle pose data source from thefirst pose measurement system to the second pose measurement system. Thepose data offset represents the difference between pose data receivedfrom the first pose measurement system (e.g., 311 a from FIG. 3A) andpose data received from the second pose measurement system (e.g., 312 afrom FIG. 3A). The pose data offset can allow the vehicle computersystem 305 a to gradually transition the vehicle pose data from posedata from a first pose measurement system to pose data from the secondpose measurement system. The gradual transition may allow for bettercontrol of the autonomous vehicle.

For example, the vehicle computer system 305 a from FIG. 3A may receivepose data from pose measurement system 311 a indicating a first positionof the autonomous vehicle at a particular time. The vehicle computersystem 305 a may also receive pose data from pose measurement system 312a indicating a second position of the autonomous vehicle at the sameparticular time. The second position indicated by pose measurementsystem 312 a may be 5 miles east of the first position indicated by posemeasurement system 311 a. Thus, the vehicle computer system maydetermine that the pose data offset is 5 miles.

The pose data quality of the pose data received from the second posemeasurement system 312 a may be higher than the pose data quality of thepose data received from the first pose measurement system 311 a. Thevehicle computer system may have initially been using the first posemeasurement system 311 a as a source for the vehicle pose data. Becausethe pose data from system 311 a has a higher quality than the pose dataof system 312 a has, the vehicle computer system 305 a may determine tochange the source of the vehicle pose data from system 311 a to system312 a.

This example may correspond to row seven of table 351 c, in which thefirst pose measurement system 311 a is providing pose data with a “bad”pose data quality, while the second pose measurement system 312 a isproviding pose data with a “good” pose data quality. In this example,the vehicle computer system 305 a changes the vehicle pose data sourceto the second pose measurement system 312 a, as shown by row seven ofcolumn 354 c, which states “pose data #2”.

Because the pose data offset is 5 miles, an immediate change by thevehicle computer system 305 a from the pose data of the first posemeasurement system to pose data of the second pose measurement systemmay result in diminished or poor control of the autonomous vehicle.Instead, the vehicle computer system 305 a may modify the vehicle posedata such that the vehicle pose data gradually transitions from thefirst pose data from system 311 a to the second pose data from system312 a. In some embodiments, the vehicle computer system 305 a may useinterpolation to gradually transition the vehicle pose data from thepose data of the first pose measurement system to the pose data of thesecond pose measurement system. The gradual transition of vehicle posedata may prevent unwanted control outputs to the autonomous vehicle inresponse to hey sudden 5 miles east change in vehicle pose data. Otherembodiments are also possible.

Column 355 c indicates the control output by the vehicle computer system(e.g. vehicle computer system 305 a from FIG. 3A) based on the vehiclepose data 354 c and the corresponding vehicle pose data quality (seecolumn 352 c or 353 c). The vehicle computer system may includetrajectory software that determines and/or outputs control outputsaffecting the autonomous vehicle driving operations. The trajectorysoftware may consume as data inputs the vehicle pose data and/or posedata quality from the pose monitor software to generate the controloutputs.

The vehicle computer system 305 a may determine a control output for theautonomous vehicle based on the pose data quality of the vehicle posedata. When the pose data quality of the vehicle pose data is “good”, thecontrol output of the vehicle computer system may be “normal” (see rowsone, two, three, four, and seven in column 355 c of table 351 c). Inother words, the vehicle computer system 305 a allows the autonomousvehicle to continue operating normally when the vehicle pose dataquality is “good.” Normal operation of the autonomous vehicle mayinclude driving at a certain speed, driving within the speed limit,turning, stopping, parking, lane changing, accelerating, decelerating,and/or other vehicle operations during normal operation of theautonomous vehicle. In some embodiments, the vehicle computer system mayonly generate a “normal” control output when the pose data quality ofthe pose data for all pose measurement systems is “good” (see row one oftable 351 c). In this case, a different control output (such as “pullover and park” or “finish the trip” or some other output) may begenerated when some, but not all, of the received pose data has a posedata quality of “good” (such as rows two, three, four, and seven oftable 351 c) to cause the vehicle to stop. For example, for rows two andfour of table 351 c, a control output of “finish the trip” may begenerated (not displayed) due to “marginal” quality pose data beingreceived from a pose measurement system, while a control output of “pullover and park” may be generated (not displayed) for rows three and sevenof table 351 c due to “bad” quality pose data being received from a posemeasurement system.

When the vehicle computer system 305 a determines that pose data qualityof the vehicle pose data is downgraded to “marginal”, the vehiclecomputer system 305 a may generate a control output to override theoperation of the autonomous vehicle. In FIG. 3C, vehicle pose data witha “marginal” pose data quality causes the vehicle computer system 305 ato generate a control output for the vehicle to pull over and park (seerows five, six, and eight of column 355 c). In some embodiments, thiscontrol output may indicate that the autonomous vehicle may pull overand park the vehicle as soon as it is safe to do so. In otherembodiments, the autonomous vehicle may pull over to a safe place andpark the vehicle within a predetermined period of time. The period oftime may be 5 seconds, 15 seconds, 60 seconds, or some other amount oftime. Other autonomous vehicle operations and/or control outputs arealso possible.

When the pose data quality of the vehicle pose data is downgraded to aquality of “bad”, the vehicle computer system 305 a may generate acontrol output of “emergency brake” to override current operation of theautonomous vehicle. In some embodiments, the control output “emergencybrake” may indicate that the autonomous vehicle will safely pull overand stop. In other embodiments, the control output “emergency brake” maycause the autonomous vehicle to stop immediately. Other autonomousvehicle operations in response to the control output are also possible.

In some embodiments, the vehicle computer system 305 a may respond todegraded pose data quality (“bad” or “marginal” pose data quality) ofthe vehicle pose data by generating a control output for assisted modedriving. During assisted mode, the vehicle control system 305 a allows aperson to control and/or drive the vehicle. In some embodiments, thevehicle control system 305 a may request the person to control and/ordrive the vehicle during assisted mode driving. In other embodiments,vehicle control system 305 a may require the person to control and/ordrive the vehicle during assisted mode driving. In some embodiments, thevehicle control system 305 a may generate a control output for assisteddriving mode only when a person is detected in the autonomous vehicle.In other embodiments, the autonomous vehicle may not have an assistedmode. Other embodiments are also possible.

Although not displayed in FIG. 3C, the vehicle computer system 305 a mayhave a hierarchy of control outputs that include more control outputsthan those displayed in table 351 c of FIG. 3C. As mentioned earlier,different control outputs may be generated for a particular pose dataquality using received and/or stored additional information. Forexample, pose data with a “marginal” pose data quality may generatemultiple types of control outputs based on the additional informationexplaining the pose data quality is “marginal.”

In one embodiment, a control output of “finish the trip” may begenerated based on vehicle pose data with a “marginal” pose data qualityand additional information indicating the yaw sensor failed by an amountgreater than a first threshold. If the yaw sensor failure amountincreases and exceeds a second threshold that is larger than a firstthreshold, then the pose data quality may remain “marginal,” but thecontrol output generated may change to “pull over and park.” In thisway, multiple control outputs may be generated for a single pose dataquality based on received and/or stored additional information. Themultiple control outputs and the outputs displayed in table 351 c may bepart of a hierarchy of control outputs generated by the vehicle computersystem 305 a based on pose data quality of the vehicle pose data and/orother additional information. Other embodiments are also possible.

FIG. 4 illustrates a flowchart showing the method 400 that may allow fordetermining vehicle pose data, according to an example embodiment. Themethod 400 may be carried out by a vehicle computer system, such as thevehicle computer system illustrated and described with respect to FIG. 1and FIG. 3A. However, other computing devices for an autonomous vehiclemay also execute method 400.

Furthermore, it is noted that the functionality described in connectionwith the flowcharts described herein can be implemented asspecial-function and/or configured general-function hardware modules,portions of program code executed by a processor for achieving specificlogical functions, determinations, and/or steps described in connectionwith the flowchart shown in FIG. 4. Where used, program code can bestored on any type of computer-readable medium, for example, such as astorage device including a disk or hard drive.

In addition, each block of the flowchart shown in FIG. 4 may representcircuitry that is wired to perform the specific logical functions in theprocess. Unless specifically indicated, functions in the flowchart shownin FIG. 4 may be executed out of order from that shown or discussed,including substantially concurrent execution of separately describedfunctions, or even in reverse order in some examples, depending on thefunctionality involved, so long as the overall functionality of thedescribed method is maintained.

As shown by block 402 of FIG. 4, method 400 may involve receiving, at avehicle computer system for an autonomous vehicle, first pose data forthe autonomous vehicle from a first pose measurement system of theautonomous vehicle, wherein the first pose measurement system includesone or more corresponding sensors of the autonomous vehicle. In someexamples, the pose data may describe a position, orientation, andvelocity of the autonomous vehicle relative to the world. In additionalexamples, the pose measurement system may include corresponding sensors,such as IMUS, wheel speed sensors, and/or a GPS receiver.

Method 400 may further involve receiving, at the vehicle computer systemfor the autonomous vehicle, second pose data for the autonomous vehiclefrom a second pose measurement system of the autonomous vehicle, whereinthe second pose measurement system includes one or more correspondingsensors of the autonomous vehicle, as shown by block 404 of FIG. 4. Insome examples, the second pose data may describe the position,orientation, and velocity of the autonomous vehicle relative to theworld. In further examples, the second pose data may be different fromthe first pose data. In additional examples, the second pose measurementsystem may include sensors, such as IMUs, GPS receivers, and wheel speedsensors, that are different from the sensors of the first posemeasurement system.

Method 400 may also involve determining a first pose data quality forthe received first pose data and a second pose data quality for thereceived second pose data, as shown by block 406 of FIG. 4. In someexamples, the first pose data quality may be determined to be “good,”“marginal,” or “bad.” In additional examples, the pose data quality maybe determined by checking if sensor data is out of range, using Kalmanfilters, or some other method for determining the quality of thereceived sensor data.

Method 400 may additionally involve setting the first pose data asvehicle pose data for the autonomous vehicle in response to the firstpose data quality being better than or the same as the second pose dataquality, as shown by block 408 of FIG. 4. In some examples, the vehiclepose data may be changed to the second pose data when the second posedata quality is better than the first pose data quality. In additionalexamples, the vehicle computer system may determine a pose data offsetbetween the first pose data and the second pose data. In furtherexamples, the pose data offset may be used to gradually transition thevehicle pose data from the first pose data to the second pose data.

Method 400 may additionally involve controlling, by the vehicle computersystem, the autonomous vehicle based on at least the vehicle pose data,as shown by block 410 of FIG. 4. In some examples, the vehicle computersystem may control the autonomous vehicle to continue normal operation.In additional examples, the vehicle computer system may control theautonomous vehicle to park safely as soon as possible. In furtherexamples, the vehicle computer system may control the autonomous vehicleto brake and stop immediately.

In some embodiments, method 400 may include more steps than thosedisplayed in FIG. 4. For example, after the vehicle pose data is set asthe first pose data (see block 408), the method 400 may also includereceiving updated first pose data and updated second pose data. Oncereceived, the method 400 may include updating the first pose dataquality and the second pose data quality based on the updated first posedata and the updated second pose data. The method 400 may furtherinclude determining the updated second pose data quality is better thanthe updated first pose data quality. In some examples, the updatedsecond pose data quality may be “good” while the updated first pose dataquality is “bad.” The method 400 may also include in response todetermining that the updated second pose data quality is better than theupdated first pose data quality, changing the vehicle pose data from thefirst pose data to the updated second pose data. In some examples, thevehicle pose data may change from the first pose data to the updatedsecond pose data because the updated second pose data quality is “good”while the updated first pose data quality is “bad.”

In some embodiments, the disclosed methods may be implemented ascomputer program instructions encoded on a non-transitorycomputer-readable storage media in a machine-readable format, or onother non-transitory media or articles of manufacture. FIG. 5illustrates an example computer readable medium in the form of acomputer program product 500 that includes a computer program forexecuting a computer process on a computing device, arranged fordetermining vehicle pose data for an autonomous vehicle. In oneembodiment, the example computer program product 500 is provided using asignal bearing medium 501. The signal bearing medium 501 may include oneor more program instructions 502 that, when executed by one or moreprocessors (e.g., processor 113 in the computing device 111) may providefunctionality or portions of the functionality described above withrespect to FIGS. 1-4. Thus, for example, referring to the embodimentsshown in FIG. 4, one or more features of blocks 402-410 may beundertaken by one or more instructions associated with the signalbearing medium 501. In addition, the program instructions 502 in FIG. 5describe example instructions as well.

In some examples, the signal bearing medium 501 may encompass acomputer-readable medium 503, such as, but not limited to, a hard diskdrive, a Compact Disc (CD), a Digital Video Disk (DVD), a digital tape,memory, etc. In some implementations, the signal bearing medium 501 mayencompass a computer recordable medium 504, such as, but not limited to,memory, read/write (R/W) CDs, R/W DVDs, etc. In some implementations,the signal bearing medium 501 may encompass a communications medium 505,such as, but not limited to, a digital and/or an analog communicationmedium (e.g., a fiber optic cable, a waveguide, a wired communicationslink, a wireless communication link, etc.). Thus, for example, thesignal bearing medium 501 may be conveyed by a wireless form of thecommunications medium 505 (e.g., a wireless communications mediumconforming to the IEEE 802.11 standard or other transmission protocol).

The one or more programming instructions 502 may be, for example,computer executable and/or logic implemented instructions. In someexamples, a computing device such as the computing device described withrespect to FIGS. 1-4 may be configured to provide various operations,functions, or actions in response to the programming instructions 502conveyed to the computing device by one or more of the computer readablemedium 503, the computer recordable medium 504, and/or thecommunications medium 505.

The present disclosure is not to be limited in terms of the particularembodiments described in this application, which are intended asillustrations of various aspects. Many modifications and variations canbe made without departing from its spirit and scope, as will be apparentto those skilled in the art. Functionally equivalent methods andapparatuses within the scope of the disclosure, in addition to thoseenumerated herein, will be apparent to those skilled in the art from theforegoing descriptions. Such modifications and variations are intendedto fall within the scope of the appended claims.

The above detailed description describes various features and functionsof the disclosed systems, devices, and methods with reference to theaccompanying figures. In the figures, similar symbols typically identifysimilar components, unless context dictates otherwise. The exampleembodiments described herein and in the figures are not meant to belimiting. Other embodiments can be utilized, and other changes can bemade, without departing from the spirit or scope of the subject matterpresented herein. It will be readily understood that the aspects of thepresent disclosure, as generally described herein, and illustrated inthe figures, can be arranged, substituted, combined, separated, anddesigned in a wide variety of different configurations, all of which areexplicitly contemplated herein.

A block that represents a processing of information may correspond tocircuitry that can be configured to perform the specific logicalfunctions of a herein-described method or technique. Alternatively oradditionally, a block that represents a processing of information maycorrespond to a module, a segment, or a portion of program code(including related data). The program code may include one or moreinstructions executable by a processor for implementing specific logicalfunctions or actions in the method or technique. The program code and/orrelated data may be stored on any type of computer readable medium suchas a storage device including a disk or hard drive or other storagemedium.

The computer readable medium may also include non-transitory computerreadable media such as computer-readable media that stores data forshort periods of time like register memory, processor cache, and randomaccess memory (RAM). The computer readable media may also includenon-transitory computer readable media that stores program code and/ordata for longer periods of time, such as secondary or persistent longterm storage, like read only memory (ROM), optical or magnetic disks,compact-disc read only memory (CD-ROM), for example. The computerreadable media may also be any other volatile or non-volatile storagesystems. A computer readable medium may be considered a computerreadable storage medium, for example, or a tangible storage device.

Moreover, a block that represents one or more information transmissionsmay correspond to information transmissions between software and/orhardware modules in the same physical device. However, other informationtransmissions may be between software modules and/or hardware modules indifferent physical devices.

The particular arrangements shown in the figures should not be viewed aslimiting. It should be understood that other embodiments can includemore or less of each element shown in a given figure. Further, some ofthe illustrated elements can be combined or omitted. Yet further, anexample embodiment can include elements that are not illustrated in thefigures.

While various aspects and embodiments have been disclosed herein, otheraspects and embodiments will be apparent to those skilled in the art.The various aspects and embodiments disclosed herein are for purposes ofillustration and are not intended to be limiting, with the true scopebeing indicated by the following claims.

What is claimed is:
 1. A method comprising: determining first pose data for an autonomous vehicle based on first sensor data from a first plurality of sensors; crosschecking the first sensor data from the first plurality of sensors; determining second pose data for the autonomous vehicle based on second sensor data from a second plurality of sensors; crosschecking the second sensor data from the second plurality of sensors; and controlling, by a vehicle computer system, motion of the autonomous vehicle based on and at least one of the first pose data or the second pose data.
 2. The method of claim 1, wherein the first plurality of sensors comprises at least one of an inertial measurement unit (IMU), a wheel speed sensor, or a Global Positioning System (GPS) receiver.
 3. The method of claim 1, wherein the second plurality of sensors comprises at least one of an inertial measurement unit (IMU), a wheel speed sensor, or a Global Positioning System (GPS) receiver.
 4. The method of claim 1, further comprising: determining first pose data quality for the first pose data, wherein the first pose data quality is determined based at least in part on the crosschecking of the first sensor data from the first plurality of sensors; and determining second pose data quality for the second pose data, wherein the second pose data quality is determined based at least in part on the crosschecking of the second sensor data from the second plurality of sensors.
 5. The method of claim 4, wherein the first pose data and the first pose data quality are determined by a first processor, and wherein the second pose data and the second pose data quality are determined by a second processor, further comprising: receiving, by the vehicle computer system, the first pose data and the first pose data quality from the first processor; and receiving, by the vehicle computer system, the second pose data and the second pose data quality from the second processor.
 6. The method of claim 4, wherein controlling motion of the autonomous vehicle based on at least one of the first pose data or the second pose data comprises: comparing the first pose data quality and the second pose data quality; controlling the autonomous vehicle based on the first pose data if the first pose data quality is higher than the second pose data quality; and controlling the autonomous vehicle based on the second pose data if the second pose data quality is higher than the first pose data quality.
 7. The method of claim 4, wherein controlling motion of the autonomous vehicle based on at least one of the first pose data or the second pose data comprises: determining whether either of the first pose data quality or the second pose data quality is at a predetermined high level; controlling the autonomous vehicle normally if at least one of the first pose data quality or the second pose data quality is at the predetermined high level; and controlling the autonomous vehicle to perform an emergency maneuver if neither the first pose data quality nor the second pose data quality is at the predetermined high level.
 8. The method of claim 7, wherein controlling the autonomous vehicle to perform the emergency maneuver comprises controlling the autonomous vehicle to pull over and park.
 9. The method of claim 7, wherein controlling the autonomous vehicle to perform the emergency maneuver comprises controlling the autonomous vehicle to stop immediately.
 10. A system, comprising: a first pose measurement system for an autonomous vehicle, comprising: a first plurality of sensors; and a first processor, wherein the first processor is configured to determine first pose data for the autonomous vehicle based on first sensor data from the first plurality of sensors and crosscheck the first sensor data from the first plurality of sensors; a second pose measurement system for an autonomous vehicle, comprising: a second plurality of sensors; and a second processor, wherein the second processor is configured to determine second pose data for the autonomous vehicle based on second sensor data from the second plurality of sensors and crosscheck the second sensor data from the second plurality of sensors; and a vehicle control system comprising a vehicle-control processor and a memory storing instructions executable by the vehicle-control processor to perform functions comprising: receiving the first pose data from the first pose measurement system; receiving the second pose data from the second pose measurement system; and controlling motion of the autonomous vehicle based on at least one of the first pose data or the second pose data.
 11. The system of claim 10, wherein the first plurality of sensors comprises at least one of an inertial measurement unit (IMU), a wheel speed sensor, or a Global Positioning System (GPS) receiver, and wherein the second plurality of sensors comprises at least one of an IMU, a wheel speed sensor, or a GPS receiver.
 12. The system of claim 10, wherein the functions further comprise: determining first pose data quality for the first pose data, wherein the first pose data quality is determined based at least in part on crosschecking the first pose data from the first plurality of sensors; and determining second pose data quality for the second pose data, wherein the second pose data quality is determined based at least in part on crosschecking the second pose data from the second plurality of sensors.
 13. The system of claim 12, wherein controlling motion of the autonomous vehicle based on at least one of the first pose data or the second pose data comprises: comparing the first pose data quality and the second pose data quality; controlling the autonomous vehicle based on the first pose data if the first pose data quality is higher than the second pose data quality; and controlling the autonomous vehicle based on the second pose data if the second pose data quality is higher than the first pose data quality.
 14. The system of claim 12, wherein controlling motion of the autonomous vehicle based on at least one of the first pose data or the second pose data comprises: determining whether either of the first pose data quality or the second pose data quality is at a predetermined high level; controlling the autonomous vehicle normally if at least one of the first pose data quality or the second pose data quality is at the predetermined high level; and controlling the autonomous vehicle to perform an emergency maneuver if neither the first pose data quality nor the second pose data quality is at the predetermined high level.
 15. The system of claim 14, wherein controlling the autonomous vehicle to perform the emergency maneuver comprises controlling the autonomous vehicle to pull over and park.
 16. The system of claim 14, wherein controlling the autonomous vehicle to perform the emergency maneuver comprises controlling the autonomous vehicle to stop immediately.
 17. A non-transitory computer-readable medium storing instructions that are executable by one or more computing devices, wherein executing the instructions causes the one or more computing devices to perform functions comprising: determining first pose data for an autonomous vehicle based on first sensor data from a first plurality of sensors; crosschecking the first sensor data from the first plurality of sensors; determining second pose data for the autonomous vehicle based on second sensor data from a second plurality of sensors; crosschecking the second sensor data from the second plurality of sensors; and controlling motion of the autonomous vehicle based on at least one of the first pose data or the second pose data.
 18. The non-transitory computer readable medium of claim 17, wherein the functions further comprise: determining first pose data quality for the first pose data, wherein the first pose data quality is determined based at least in part on the crosschecking of the first sensor data from the first plurality of sensors; and determining second pose data quality for the second pose data, wherein the second pose data quality is determined based at least in part on the crosschecking of the second sensor data from the second plurality of sensors.
 19. The non-transitory computer readable medium of claim 18, wherein controlling motion of the autonomous vehicle based on at least one of the first pose data or the second pose data comprises: comparing the first pose data quality and the second pose data quality; controlling the autonomous vehicle based on the first pose data if the first pose data quality is higher than the second pose data quality; and controlling the autonomous vehicle based on the second pose data if the second pose data quality is higher than the first pose data quality.
 20. The non-transitory computer readable medium of claim 18, wherein controlling motion of the autonomous vehicle based on at least one of the first pose data or the second pose data comprises: determining whether either of the first pose data quality or the second pose data quality is at a predetermined high level; controlling the autonomous vehicle normally if at least one of the first pose data quality or the second pose data quality is at the predetermined high level; and controlling the autonomous vehicle to perform an emergency maneuver if neither the first pose data quality nor the second pose data quality is at the predetermined high level. 